Skip to content
Hands-On Reinforcement Learning with Python, published by Packt
Branch: master
Clone or download
jovita1195
jovita1195 Update README.md
Latest commit 27405a6 Jul 6, 2018
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
Chapter01 Code files Jun 28, 2018
Chapter02 Code files Jun 28, 2018
Chapter03 Code files Jun 28, 2018
Chapter04 Code files Jun 28, 2018
Chapter05 Code files Jun 28, 2018
Chapter06 Code files Jun 28, 2018
Chapter07 Code files Jun 28, 2018
Chapter08 Code files Jun 28, 2018
Chapter09 Code files Jun 28, 2018
Chapter10 Code files Jun 28, 2018
Chapter11 Code files Jun 28, 2018
Chapter12 Code files Jun 28, 2018
Chapter13 Code files Jun 28, 2018
LICENSE Initial commit Jun 28, 2018
README.md Update README.md Jul 6, 2018

README.md

Hands-On-Reinforcement-Learning-with-Python

Hands-On Reinforcement Learning with Python

This is the code repository for Hands-On-Reinforcement-Learning-with-Python, published by Packt.

Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow

What is this book about?

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms.

This book covers the following exciting features:

  • Understand the basics of reinforcement learning methods, algorithms, and elements
  • Train an agent to walk using OpenAI Gym and Tensorflow
  • Understand the Markov Decision Process, Bellman’s optimality, and TD learning
  • Solve multi-armed-bandit problems using various algorithms
  • Master deep learning algorithms, such as RNN, LSTM, and CNN with applications

If you feel this book is for you, get your copy today!

https://www.packtpub.com/

Instructions and Navigations

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

policy_iteration():
Initialize random policy
for i in no_of_iterations:
Q_value = value_function(random_policy)
new_policy = Maximum state action pair from Q value

Following is what you need for this book: If you’re a machine learning developer or deep learning enthusiast interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book.

With the following software and hardware list you can run all code files present in the book (Chapter 1-15).

Software and Hardware List

Chapter Software required OS required
1-12 anaconda Ubutnu or mac
chrome Ubutnu or mac

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

Related products

Get to Know the Author

Sudharsan Ravichandiran is a data scientist, researcher, artificial intelligence enthusiast, and YouTuber (search for Sudharsan reinforcement learning). He completed his bachelors in information technology at Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning, which includes natural language processing and computer vision. He used to be a freelance web developer and designer and has designed award-winning websites. He is an open source contributor and loves answering questions on Stack Overflow.

Suggestions and Feedback

Click here if you have any feedback or suggestions.

You can’t perform that action at this time.